Fall detection through acoustic Local Ternary Patterns

Adnan, Syed Muhammad; Irtaza, Aun; Aziz, Sumair; Ullah, Muhammad Obaid; Javed, Ali; Mahmood, Muhammad Tariq

doi:10.1016/j.apacoust.2018.06.013

Cited by 49 publications

(27 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LTP is a statistical approach for textural analysis in image processing [42]. It encodes the density differences between central and neighboring pixels as -1, 0, and 1 [43]. One dimensional LTP (1D-LTP) has been improved from LTP for speech signal frames; it compares each value with its neighbors according to a threshold value.…”

Section: B One Dimensional Local Ternary Patternmentioning

confidence: 99%

“…where ( , , ) is ith value of the signal frame is the ith block value of the nine blocks, is the center -the 5th block, and is the threshold value [42], [43]. The magnitude differences between ( ± ) and are calculated.…”

Section: B One Dimensional Local Ternary Patternmentioning

confidence: 99%

“…In Fig. 4, the threshold t is 8, but threshold changes depending on the purpose of the application [43].…”

Section: B One Dimensional Local Ternary Patternmentioning

confidence: 99%

See 2 more Smart Citations

A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Sönmez

Varol

2020

IEEE Access

View full text Add to dashboard Cite

Interpreting a speech signal is quite challenging because it consists of different frequencies and features that vary according to emotions. Although different algorithms are being developed in the speech emotion recognition (SER) domain, the success rates vary according to the spoken languages, emotions, and databases. In this study, a new lightweight effective SER method has been developed that has low computational complexity. This method, called 1BTPDN, is applied on RAVDESS, EMO-DB, SAVEE, and EMOVO databases. First, low-pass filter coefficients are obtained by applying a one-dimensional discrete wavelet transform on the raw audio data. The features are extracted by applying textural analysis methods, a one-dimensional local binary pattern, and a one-dimensional local ternary pattern to each filter. Using neighborhood component analysis, the most dominant 1024 features are selected from 7680 features while the other features are discarded. These 1024 features are selected as the input of the classifier which is a thirddegree polynomial kernel-based support vector machine. The success rates of the 1BTPDN reached 95.16%, 89.16%, 76.67%, and 74.31% in the RAVDESS, EMO-DB, SAVEE, and EMOVO databases, respectively. The recognition rates are higher compared to many textural, acoustic, and deep learning state-of-the-art SER methods. INDEX TERMS Discrete wavelet transform, local binary pattern, local ternary pattern, neighborhood component analysis, speech emotion recognition.

show abstract

Section: B One Dimensional Local Ternary Patternmentioning

confidence: 99%

Section: B One Dimensional Local Ternary Patternmentioning

confidence: 99%

See 1 more Smart Citation

A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Sönmez

Varol

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The main objective of this method was to improve facial recognition ability of the LBP. Adnan et al [23] presented a 1D local ternary pattern (LTP) for fall detection from audio signals. LTP is similar to LBP but LTP uses ternary function to extract binary features and 512 dimensions of feature are extracted using LTP.…”

Section: A Related Workmentioning

confidence: 99%

Discrete Complex Fuzzy Transform based Face Image Recognition Method

Tuncer¹,

Doğan²,

Akbal³

2019

IJIGSP

View full text Add to dashboard Cite

In this paper, a novel discrete complex fuzzy transform (DCFT) and the proposed DCFT based facial image recognition method is presented. The presented DCFT consists of histogram extraction, peak points of histogram calculation and images construction. 3 real and 3 complex images are constructed using DCFT. Also, 3 angular images and 3 vector image are calculated using the real and complex images. To create real and complex images, polynomial and smith fuzzy sets are used in this paper. Briefly, 12 image are constructed using DCFT. In order to demonstrate effect of the proposed DCFT, face images data sets and local binary pattern (LBP) are used to create facial image recognition method. In this method, LBP is applied on the each DCFT image and 12 x 256 size of feature are extracted. Also, maximum pooling is applied on this feature set to obtain 256 size of feature. In the classification phase, support vector machine (SVM) and k nearest neighborhood (KNN) classifiers are used. The comparisons clearly demonstrate that the proposed DCFT is increased facial image recognition capability.

show abstract

“…[3][4][5][6][7] The environmental variable analysis method uses one or more sensors to detect environmental changes in a certain space to collect information of the body so as to determine whether the falls occur. The commonly used sensors include infrared sensors, 8 audio sensor, 9,10 vibration sensor, 11,12 radio frequency (RF) signal, 13,14 and so on. However, there are some problems in the previous methods, such as limited monitoring region, privacy exposure, cost inefficient, and vulnerable to the environment.…”

Section: Introductionmentioning

confidence: 99%

The real-time elderly fall posture identifying scheme with wearable sensors

Sun

et al. 2019

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

The accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at the waist to monitor the attitude of the body with triaxial accelerometer and gyroscope. The graphene/rubber sensors are located at the knees to monitor the moving actions of the legs. A real-time fall postures identifying algorithm is proposed by the integration of triaxial accelerometer, tilt angles, and the bending angles from the graphene/rubber sensors. A volunteer is engaged to emulate elderly physical behaviors in performing four activities of daily living and six fall postures. Four basic fall down postures can be identified with MPU6050. Integrated with graphene/rubber sensors, two more fall postures are correctly identified by the proposed scheme. Test results show that the accuracy for activities of daily living detection is 93.5% and that for fall posture identifying is 90%. After the fall postures are identified, the proposed system transmits the fall posture to the smart phone carried by the elderly via Bluetooth. Finally, the posture and location are transmitted to the specified mobile phone by short message.

show abstract

Fall detection through acoustic Local Ternary Patterns

Cited by 49 publications

References 25 publications

A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

A Speech Emotion Recognition Model Based on Multi-Level Local Binary and Local Ternary Patterns

Discrete Complex Fuzzy Transform based Face Image Recognition Method

The real-time elderly fall posture identifying scheme with wearable sensors

Contact Info

Product

Resources

About